The argument that reinforcement learning is an appropriate framework for computational approaches to curiosity has been embraced by many authors over the past few decades.
The mechanisms that have been inspired by curiosity vary widely, with many using the amount of error in their machine-learning predictions (`prediction error') or ideas from information theory in the interests of simulating other constructs, like confidence [1]}, learning progress [2]}, surprise ([3]}), interest/interestingness ([4]}; [5]}), novelty ([4]}; [7]}), uncertainty [8]}, compression progress ([9]}),
competence ([10]}),
and information gain ([11]}; [12]}; [13]}; [5]}).
